From: Big data are coming to psychiatry: a general introduction
Description | Primary finding | Number of subjects (n) | Data source | References |
---|---|---|---|---|
Create actuarial suicide risk algorithm to predict suicide in the 12Â months after inpatient hospitalization for psychiatric disorder | 52.9Â % of posthospitalization suicides occurred after the 5Â % of hospitalizations with the highest predicted suicide risk | 40,820 soldiers hospitalized for psychiatric disorders. 421 predictors | 38 army and DOD administrative data | Kessler et al. (2015) |
Explore prevalence of substance use disorders (SUD) among psychiatric patients in large university system | 24.9 % of patients had SUD; SUD associated with more inpatient and emergency care | 40,999 psychiatric patients aged 18–64 years who sought treatment between 2000 and 2010 | EMR-based psychiatry registry | Wu et al. (2013) |
Ongoing study of cognitive impairment using neuroimaging and genetics | Neuroimaging phenotypes were significantly associated with progression of dementia | 808 patients over age 65, including 200 with Alzheimer’s disease | 20 derived neuroimaging markers plus 20 SNPs | Weiner et al. (2012) |
Examine use of psychotropic drugs by patients without psychiatric diagnosis | 58Â % of those prescribed a psychiatric medication in 2009 had no psychiatric diagnosis | 5,132,789 individuals who received prescription for psychotropic medication | Private medication claims database | Wiechers et al. (2013) |
Analyze prescribing of psychotropic drugs by specialty | 59 % written by general practitioners, 23 % by psychiatrists, 17 % by other physicians and providers | 472 million prescriptions for psychotropic drugs | IMS database of 70 % of US retail pharmacy transactions for 2006–2007 | Mark et al. (2009) |
Compare risk of dementia in those 55 or older having traumatic (TBI) brain injury versus non-TBI trauma (NTT) | TBI increased risk for dementia over NTT | 51,799 patients with trauma, of which 31.5Â % had TBI | CA statewide administrative health database of ER and inpatient visits | Gardner et al. (2014) |
Use machine learning to predict suicidal behavior text in EMR | Model obtained high specificity but low sensitivity, with PPV of 41Â % | 250,000 US veterans of Gulf War | Clinical records | Ben-Ari and Hammond (1991) |
Investigate association between maternal and paternal age and risk of autism | Both increasing maternal age and increasing paternal age were independently associated with increased risk of autism | 7,550,026 single births in CA 1989–2002. 23,311 with autism | Developmental services administrative data, birth certificate data | Grether et al. (2009) |
Use natural language processing (NLP) to classify current mood state to identify treatment resistant depression | NLP models better than those relying on billing data alone | 127,504 patients with diagnosis of major depression | EMR and billing data from outpatient psychiatry practices affiliated with large hospital | Perlis et al. (2012) |
Analyze impact of Medicaid prior authorization for atypical antipsychotics on prevalence of schizophrenia among prison inmates | Prior authorization associated with greater prevalence of mental illness in inmates | 16,844 inmates | Nationally representative sample from Census Bureau | Goldman et al. (2014) |
Investigate incidence of severe psychiatric disorders following hospital contact for head injury | Increased risk of schizophrenia, depression, bipolar disorder and organic mental disorders following head injuries | 113,906 people who had suffered head injuries, and were born between 1977 and 2000 | Danish psychiatric central register | Orlovska et al. (2014) |
Integrate depression screening, prescription fulfillment and EMR to improve care in primary care (PC) | Integration improved diagnosis and management of depression in PC | 61,464 patients in PC in 14 clinical organizations | EMR, plus 4900 PHQ-9 questionnaires, plus fulfillment data for 55Â % of patients | Valuck et al. (2012) |
Analyze if SSRI/SNRI use prior to admission to ICU increased mortality risk | Increased hospital morality among those in ICU taking SSRI/SNRI before admission | 14,709 patients with 2471 taking SSRI/SNRI | Multiparameter Intelligent Monitoring in Intensive Care database (data from EMR) | Ghassemi et al. (2014) |
Evaluate safety of antipsychotic (AP) medication use in nursing homes | Dose-dependent increased risks of serious medical events such as myocardial infarction, stroke, infection, hip fracture, within 180 days of initiating AP treatment | 83,959 Medicaid eligible residents ≥age 65 who initiated AP use after nursing home admission | Medicare and Medicaid claims from 45 states | Huybrechts et al. (2012) |
Evaluate use of EMR to assist with phenotyping in bipolar disorder (BP) | Semiautomated data mining of EHR may assist with phenotyping of patients and controls | 52,235 patients with at least one diagnosis of BP or mania, spanning 20Â years | EMR, billing and inpatient pharmacy data | Castro et al. (2015) |